Fiona Adamson

Optimization under Uncertainty using DSI

Optimization under uncertainty is notoriously numerically intensive. However its numerical burden can be reduced if data space inversion (DSI) is used to construct a surrogate statistical model that can be used in place of the numerical model. This tutorial explores how new ideas from the petroleum industry can be explored using programs from the PEST […]

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ENSI and Linear Analysis

Ensemble space inversion (ENSI) enables efficient, regularisation-constrained calibration of complex, highly-parameterised models. This tutorial demonstrates how linear analysis can be undertaken in partnership with the ENSI calibration. This provides estimates of parameter and predictive uncertainty at minimal numerical cost. This tutorial is a continuation of another GMDSI tutorial. These tutorials can be done independently or

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Ensemble Space Inversion

Ensemble space inversion (ENSI) is implemented through the PEST_HP suite (version 18). Using ENSI you can calibrate a complex model quickly. The calibration subspace is comprised of random parameter realisations as well as individual parameters. Realisations can be different for different parameter types. Regularisation seeks a minimum error variance solution within the confines of the working

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From Site Concepts to a 3D Site Model

Building and history-matching a three-dimensional model is a difficult procedure. The third dimension increases parameter requirements, model run times, and model output uncertainty. Ideally, predictive uncertainty can be reduced through assimilating information from site characterisation on the one hand, and measurements of system behaviour on the other hand. But this is so much harder in

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PEST Course: Brisbane

Description Where: EcoSciences Precinct, Dutton Park, Brisbane When: Monday 3rd June to Friday 7th June, 2024 Who should attend: Both new and experienced modellers will benefit from the course, as well as anyone who would simply like to understand the theory and practice behind simulation-based data processing and environmental forecasting. Topic: “PEST” refers to a software package and

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Conceptual Model to Numerical Model

This tutorial explores the use of “conceptual points” as a precursor to model parameterisation. Expected hydraulic properties are provided at these conceptual points. Just as importantly, so-called “hyperparameters” are also ascribed to these points; these are used to characterise the nature of hydraulic property heterogeneity as it is likely to prevail in their vicinity. All

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Data Assimilation for a Simple Model

This tutorial explores construction of the interface between PEST/PEST++and a simple MODFLOW/MODPATH model, and how to then subject that model to history-matching and uncertainty analysis–including data space inversion (DSI).There is some overlap with a previous tutorial. However, there are also some important differences. Use of the PLPROC, TPL2PST and PESTPREP2 utilities are explained. The first

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Live session recordings

Week 1 NZ/AUS https://vimeo.com/866196071%20 This is the first recording from the NZ/AUS self-paced guided study course on Decision Support Groundwater Modeling with Python. Week 1 covered the Freyberg model, Bayes equation, and the PEST interface.Presenters: Jeremy White, Rui Hugman and Brioch Hemmings USA https://vimeo.com/866312617%20 This is the first recording from the USA self-paced guided study

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Webinar: Applied decision support groundwater modelling with python

GMDSI and the USGS have co-funded the development of a series of jupyter notebooks that use python scripting to demonstrate many aspects of applied decision-support groundwater modelling, from introductions to concepts through to complete modelling workflows.  This webinar presented and discussed the design and content of these notebooks, including how to get started using them,

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Structural Overlay Parameters and PLPROC

Using the PLPROC parameter preprocessor supplied with the PEST suite, moveable polylinear and polygonal structural features such as faults and aquitard windows can be inserted into a model. These features can be assigned to one or many model layers. Hydraulic properties can vary along and within them. If appropriate, the hydraulic properties associated with structural

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